Deformable MRI-TRUS surface registration from statistical deformation models of the prostate
Bibliographic record
Abstract
Transrectal ultrasound (TRUS) is considered the standard of care for imaging the prostate during biopsy and brachytherapy procedures. However, interpretation of TRUS images is challenging due to high specularity, making it difficult to recognize prostate boundaries. Image-guided brachytherapy and fusion-guided prostate biopsies require accurate non-rigid registration of magnetic resonance pre-operative image to intra-operative TRUS. State of the art techniques suggest semi-automated segmentation of the prostate on the TRUS images. However, due to the high variability, segmentation of the prostate is challenging. Segmentation errors could lead into poor localization of the biopsy target and can impact the registration of pre-operative images. In general, this kind of registration is challenging since the prostate anatomy undergoes motion due to TRUS probe pressure. In this paper, we propose a non-rigid surface registration approach for MR-TRUS fusion based on a statistical deformation model. Our method builds a statistical deformation model (SDM) of pre-operative to intra-operative deformations on a prostate dataset. In order to compute the fusion for an unseen MR-TRUS pair, the trained SDM is incorporated into the registration process to increase the fusion accuracy. The proposed approach is evaluated on a dataset of 23 patients with prostate cancer, for which the MRI-TRUS scans were available. We compared the proposed non-rigid SDM registration to non-rigid Iterative closest point (NICP) and rigid ICP approaches. Experiments demonstrate that the proposed SDM based method outperforms both NICP and ICP approaches, yielding a mean squared distance of 0.52 ± 0.26mm at the base, 0.45 ± 0.17mm mid-gland and 0.59 ± 0.13mm at the apex. These results show the advantage of integrating prior knowledge of deformation fields due to probe pressure for MR-TRUS fusion prostate interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".